Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	35.0	48.872408
daal4py_KNeighborsClassifier	0.0	6.0	30.009766
KNeighborsClassifier_kd_tree	0.0	2.0	47.116247
daal4py_KNeighborsClassifier_kd_tree	0.0	0.0	32.654761
KMeans_tall	0.0	0.0	28.166049
daal4py_KMeans_tall	0.0	0.0	11.760189
KMeans_short	0.0	0.0	3.920000
daal4py_KMeans_short	0.0	0.0	2.106013
LogisticRegression	0.0	0.0	27.950741
daal4py_LogisticRegression	0.0	0.0	6.264059
Ridge	0.0	0.0	12.554886
daal4py_Ridge	0.0	0.0	2.720863
HistGradientBoostingClassifier	0.0	5.0	7.354109
lightgbm	0.0	5.0	1.804023
xgboost	0.0	5.0	49.684208
catboost	0.0	5.0	25.901929
total	1.0	8.0	38.959091

KNeighborsClassifier vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	brute

	estimator	function	n_samples_train	n_samples	n_features	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	0.426	0.000	1.877	0.000	-1	5	NaN	NaN	0.603	0.000	0.707	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	44.889	0.000	0.000	0.045	-1	5	0.824	0.707	4.612	0.057	9.732	0.120	See	See
2	KNeighborsClassifier	predict	1000000	1	100	0.235	0.020	0.000	0.235	-1	5	1.000	1.000	0.122	0.003	1.932	0.171	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	0.151	0.000	5.281	0.000	-1	1	NaN	NaN	0.605	0.000	0.250	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	35.341	0.000	0.000	0.035	-1	1	0.735	0.940	4.680	0.049	7.552	0.080	See	See
5	KNeighborsClassifier	predict	1000000	1	100	0.230	0.018	0.000	0.230	-1	1	1.000	1.000	0.122	0.004	1.888	0.158	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	0.171	0.000	4.679	0.000	-1	100	NaN	NaN	0.593	0.000	0.288	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	44.529	0.000	0.000	0.045	-1	100	0.950	0.803	4.597	0.033	9.687	0.069	See	See
8	KNeighborsClassifier	predict	1000000	1	100	0.215	0.014	0.000	0.215	-1	100	1.000	1.000	0.119	0.001	1.803	0.116	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	0.156	0.000	5.127	0.000	1	5	NaN	NaN	0.587	0.000	0.266	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	28.855	0.120	0.000	0.029	1	5	0.824	0.803	4.611	0.050	6.258	0.073	See	See
11	KNeighborsClassifier	predict	1000000	1	100	0.264	0.009	0.000	0.264	1	5	1.000	1.000	0.120	0.003	2.192	0.089	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	0.150	0.000	5.338	0.000	1	100	NaN	NaN	0.600	0.000	0.250	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	28.751	0.180	0.000	0.029	1	100	0.950	0.707	4.606	0.029	6.242	0.055	See	See
14	KNeighborsClassifier	predict	1000000	1	100	0.259	0.004	0.000	0.259	1	100	1.000	1.000	0.123	0.004	2.112	0.076	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	0.153	0.000	5.212	0.000	1	1	NaN	NaN	0.618	0.000	0.248	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	18.784	0.097	0.000	0.019	1	1	0.735	0.940	4.678	0.027	4.015	0.031	See	See
17	KNeighborsClassifier	predict	1000000	1	100	0.249	0.003	0.000	0.249	1	1	1.000	1.000	0.121	0.002	2.062	0.042	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	0.059	0.000	0.272	0.000	-1	5	NaN	NaN	0.108	0.000	0.544	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	34.622	0.000	0.000	0.035	-1	5	0.980	0.971	0.992	0.011	34.906	0.370	See	See
20	KNeighborsClassifier	predict	1000000	1	2	0.035	0.003	0.000	0.035	-1	5	1.000	1.000	0.005	0.000	7.377	0.867	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	0.058	0.000	0.274	0.000	-1	1	NaN	NaN	0.107	0.000	0.545	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	27.451	0.162	0.000	0.027	-1	1	0.973	0.986	1.087	0.015	25.253	0.384	See	See
23	KNeighborsClassifier	predict	1000000	1	2	0.021	0.001	0.000	0.021	-1	1	1.000	1.000	0.005	0.000	4.242	0.437	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	0.062	0.000	0.259	0.000	-1	100	NaN	NaN	0.108	0.000	0.571	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	34.558	0.000	0.000	0.035	-1	100	0.981	0.982	1.003	0.011	34.458	0.393	See	See
26	KNeighborsClassifier	predict	1000000	1	2	0.036	0.002	0.000	0.036	-1	100	1.000	1.000	0.005	0.000	7.568	0.736	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	0.057	0.000	0.278	0.000	1	5	NaN	NaN	0.107	0.000	0.535	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	19.738	0.113	0.000	0.020	1	5	0.980	0.982	0.996	0.007	19.814	0.176	See	See
29	KNeighborsClassifier	predict	1000000	1	2	0.030	0.002	0.000	0.030	1	5	1.000	1.000	0.005	0.000	6.304	0.520	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	0.061	0.000	0.264	0.000	1	100	NaN	NaN	0.108	0.000	0.559	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	19.764	0.188	0.000	0.020	1	100	0.981	0.971	1.001	0.015	19.751	0.354	See	See
32	KNeighborsClassifier	predict	1000000	1	2	0.029	0.001	0.000	0.029	1	100	1.000	1.000	0.005	0.000	6.224	0.482	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	0.061	0.000	0.262	0.000	1	1	NaN	NaN	0.111	0.000	0.550	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	12.577	0.068	0.000	0.013	1	1	0.973	0.986	1.103	0.023	11.397	0.249	See	See
35	KNeighborsClassifier	predict	1000000	1	2	0.015	0.001	0.000	0.015	1	1	1.000	1.000	0.005	0.001	2.780	0.430	See	See

KNeighborsClassifier_kd_tree vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	kd_tree

	estimator	function	n_samples_train	n_samples	n_features	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.103	0.000	0.026	0.000	-1	1	NaN	NaN	0.794	0.000	3.909	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.487	0.013	0.000	0.000	-1	1	0.956	0.989	0.660	0.022	0.739	0.032	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	1	1.000	1.000	0.001	0.000	3.299	1.134	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2.925	0.000	0.027	0.000	1	100	NaN	NaN	0.824	0.000	3.551	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	4.855	0.043	0.000	0.005	1	100	0.980	0.971	0.116	0.005	41.795	1.764	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	1	100	1.000	1.000	0.000	0.000	7.719	4.014	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2.918	0.000	0.027	0.000	1	1	NaN	NaN	0.807	0.000	3.615	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.754	0.008	0.000	0.001	1	1	0.956	0.971	0.119	0.003	6.357	0.191	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	3.398	1.908	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.000	0.000	0.027	0.000	-1	5	NaN	NaN	0.813	0.000	3.690	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.884	0.016	0.000	0.001	-1	5	0.974	0.989	0.661	0.022	1.338	0.051	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.001	0.000	0.004	-1	5	1.000	1.000	0.001	0.000	3.685	1.304	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.176	0.000	0.025	0.000	1	5	NaN	NaN	0.777	0.000	4.087	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.508	0.025	0.000	0.002	1	5	0.974	0.985	0.216	0.007	6.970	0.241	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.001	0.000	0.001	1	5	1.000	1.000	0.000	0.000	3.497	2.224	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2.943	0.000	0.027	0.000	-1	100	NaN	NaN	0.751	0.000	3.920	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.006	0.042	0.000	0.003	-1	100	0.980	0.985	0.220	0.010	13.656	0.641	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.006	0.001	0.000	0.006	-1	100	1.000	1.000	0.000	0.000	14.973	7.423	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	0.875	0.000	0.018	0.000	-1	1	NaN	NaN	0.547	0.000	1.598	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	0.038	0.004	0.000	0.000	-1	1	0.976	0.986	0.008	0.001	4.770	0.635	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	0.003	0.000	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	14.787	6.599	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	0.807	0.000	0.020	0.000	1	100	NaN	NaN	0.572	0.000	1.410	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	0.057	0.005	0.000	0.000	1	100	0.985	0.975	0.001	0.000	62.704	17.748	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	4.702	3.491	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	0.818	0.000	0.020	0.000	1	1	NaN	NaN	0.543	0.000	1.508	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	0.041	0.002	0.000	0.000	1	1	0.976	0.975	0.001	0.000	42.020	13.488	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.013	3.306	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	0.860	0.000	0.019	0.000	-1	5	NaN	NaN	0.595	0.000	1.446	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	0.038	0.002	0.000	0.000	-1	5	0.986	0.986	0.008	0.001	4.610	0.753	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	0.003	0.000	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	13.690	8.864	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	0.811	0.000	0.020	0.000	1	5	NaN	NaN	0.550	0.000	1.475	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	0.034	0.001	0.000	0.000	1	5	0.986	0.988	0.001	0.000	26.232	7.965	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	4.495	2.609	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	0.809	0.000	0.020	0.000	-1	100	NaN	NaN	0.542	0.000	1.493	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	0.062	0.003	0.000	0.000	-1	100	0.985	0.988	0.001	0.000	46.863	12.264	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	0.003	0.001	0.000	0.003	-1	100	1.000	1.000	0.000	0.000	17.742	7.972	See	See

KMeans_tall vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.712	0.000	0.674	0.000	random	NaN	30	NaN	0.382	0.0	1.865	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.002	0.000	0.252	0.000	random	0.001	30	0.001	0.000	0.0	8.276	4.066	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.002	0.000	0.000	0.002	random	1.000	30	1.000	0.000	0.0	7.754	4.836	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.679	0.000	0.707	0.000	k-means++	NaN	30	NaN	0.340	0.0	1.999	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.002	0.000	0.271	0.000	k-means++	0.001	30	0.001	0.000	0.0	7.090	3.421	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.002	0.001	0.000	0.002	k-means++	1.000	30	1.000	0.000	0.0	8.451	8.133	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	8.522	0.000	2.816	0.000	random	NaN	30	NaN	4.654	0.0	1.831	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	11.978	0.000	random	0.002	30	0.002	0.000	0.0	5.653	1.735	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.002	0.000	0.014	0.002	random	1.000	30	1.000	0.000	0.0	8.244	5.081	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	8.616	0.000	2.786	0.000	k-means++	NaN	30	NaN	4.383	0.0	1.966	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.000	11.935	0.000	k-means++	0.002	30	0.002	0.000	0.0	5.716	2.838	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.002	0.000	0.014	0.002	k-means++	1.000	30	1.000	0.000	0.0	8.359	4.985	See	See

KMeans_short vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	20
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.343	0.0	0.009	0.000	k-means++	NaN	20	NaN	0.058	0.0	5.916	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.143	0.000	k-means++	-0.001	20	0.002	0.001	0.0	3.032	0.447	See	See
2	KMeans_short	predict	10000	1	2	20	0.002	0.0	0.000	0.002	k-means++	1.000	20	1.000	0.000	0.0	9.293	4.319	See	See
3	KMeans_short	fit	10000	10000	2	20	0.111	0.0	0.029	0.000	random	NaN	20	NaN	0.151	0.0	0.738	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.146	0.000	random	-0.001	20	0.002	0.001	0.0	2.838	0.757	See	See
5	KMeans_short	predict	10000	1	2	20	0.002	0.0	0.000	0.002	random	1.000	20	1.000	0.000	0.0	8.217	4.563	See	See
6	KMeans_short	fit	10000	10000	100	20	1.240	0.0	0.129	0.000	k-means++	NaN	20	NaN	0.286	0.0	4.338	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.003	0.0	4.595	0.000	k-means++	0.305	20	0.333	0.002	0.0	1.992	0.265	See	See
8	KMeans_short	predict	10000	1	100	20	0.002	0.0	0.009	0.002	k-means++	1.000	20	1.000	0.000	0.0	7.257	3.490	See	See
9	KMeans_short	fit	10000	10000	100	20	0.355	0.0	0.450	0.000	random	NaN	20	NaN	0.696	0.0	0.510	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.003	0.0	4.629	0.000	random	0.281	20	0.315	0.002	0.0	1.996	0.218	See	See
11	KMeans_short	predict	10000	1	100	20	0.002	0.0	0.009	0.002	random	1.000	20	1.000	0.000	0.0	7.591	2.965	See	See

LogisticRegression vs daal4py

All estimators share the following hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	17.117	0.0	[-0.06892986]	0.000	NaN	NaN	NaN	NaN	NaN	3.327	0.0	5.144	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[42.58797544]	0.000	NaN	NaN	NaN	NaN	0.514	0.000	0.0	0.822	0.352	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.13529397]	0.000	NaN	NaN	NaN	NaN	0.000	0.000	0.0	0.426	0.360	See	See
3	LogisticRegression	fit	1000	1000	10000	[26]	1.413	0.0	[1.47170724]	0.001	NaN	NaN	NaN	NaN	NaN	1.194	0.0	1.184	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[26]	0.003	0.0	[77.61047496]	0.000	NaN	NaN	NaN	NaN	0.300	0.004	0.0	0.660	0.066	See	See
5	LogisticRegression	predict	1000	1	10000	[26]	0.000	0.0	[15.3833638]	0.000	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.140	0.088	See	See

Ridge vs daal4py

All estimators share the following hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	deprecated
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.345	0.000	0.232	0.0	NaN	NaN	NaN	0.339	0.000	1.017	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.013	0.001	5.984	0.0	NaN	NaN	0.12	0.023	0.001	0.588	0.048	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.000	0.659	0.0	NaN	NaN	NaN	0.000	0.000	0.575	0.354	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.698	0.000	0.471	0.0	NaN	NaN	NaN	0.443	0.000	3.834	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.000	3.824	0.0	NaN	NaN	1.00	0.000	0.000	0.705	0.408	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.000	0.008	0.0	NaN	NaN	NaN	0.000	0.000	0.631	0.587	See	See

Environment information

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}